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Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN
Specific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. In this study, a novel multitask bi-directional RNN...
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| Опубликовано в: : | PLoS One |
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| Главные авторы: | , , , , , , |
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Public Library of Science
2019
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| Предметы: | |
| Online-ссылка: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6497281/ https://ncbi.nlm.nih.gov/pubmed/31048840 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0216046 |
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